| Title: |
Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort |
| Authors: |
Ceccato, Adrián; Forne Izquierdo, Carles; Bos, Lieuwe D.; Camprubí Rimblas, Marta; Areny Balagueró, Aina; Campaña Duel, Elena; Quero Blanca, Sara; Diaz Santos, Emili; Roca, Oriol; Gonzalo Calvo, David de; Fernández Barat, Laia; Motos, Anna; Ferrer Roca, Ricard; Riera del Brío, Jordi; Lorente Balanza, José Ángel; Peñuelas Rodríguez, Óscar; Menéndez, Rosario; Amaya Villar, Rosario; Añón Elizalde, José Manuel; Balan Mariño, Ana; Barberà, Carme; Barberán, José; Blandino Ortiz, Aaron Rolando; Boado Varela, María Victoria; Bustamante Munguira, Elena; Caballero, Jesús; Carbajales Pérez, Cristina; Carbonell, Nieves; Catalán González, Mercedes; Franco, Nieves; Galbán, Cristóbal; Gumucio Sanguino, Victor Daniel; Torre, María del Carmen de la; Estella, Ángel; Gallego Curto, Elena; García Garmendia, José Luis; Garnacho Montero, José; Gómez García, José Manuel; Huerta, Arturo; Jorge García, Ruth Noemí; Loza Vázquez, Ana; Marin Corral, Judith; Martínez de la Gándara, Amalia; Martín Delgado, María Cruz; Martínez Varela, Ignacio; López Messa, Juan; Muñiz Albaiceta, Guillermo; Nieto, María Teresa; Novo, Mariana Andrea; Peñasco Martín, Yhivian; Pozo Laderas, Juan Carlos; Pérez García, Felipe; Ricart Martí, Pilar; Roche Campo, Ferran; Rodríguez Oviedo, Alejandro Hugo; Sagredo Meneses, Víctor; Sánchez Miralles, Angel; Sancho Chinesta, Susana; Socias Crespí, Lorenzo; Solé Violan, Jordi; Suarez Sipmann, Fernando; Tamayo Lomas, Luis; Trenado Álvarez, José; Úbeda Iglesias, Alejandro; Valdivia, Luis Jorge; Vidal Cortés, Pablo; Bermejo Martín, Jesús Francisco; González Gutiérrez, Jéssica; Barbé Illa, Ferran; Calfee, Carolyn S.; Artigas Raventós, Antonio; Torres Martí, Antoni |
| Contributors: |
Universidad de Alcalá. Departamento de Biomedicina y Biotecnología; Unidad Docente Microbiología |
| Publication Year: |
2024 |
| Collection: |
e_Buah - Biblioteca Digital de la Universidad de Alcalá |
| Subject Terms: |
ARDS; Clustering; Mortality; Precision medicine; Medicina; Medicine |
| Description: |
12 p. ; Background Acute respiratory distress syndrome (ARDS) can be classifed into sub-phenotypes according to diferent infammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinfammatory or hyperinfammatory sub-phenotypes, even though the time of analysis may change the classifcation according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster. Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-proto? types) using continuous and categorical readily available variables at baseline and day 3. Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, infammatory bio? markers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clus? tering processes (43.8% [n=1025] versus 27.3% [n=383] at baseline, and 49% [n=1023] versus 20.6% [n=321] on day 3). Four hundred and ffty-eight (33%) patients clustered in the frst group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the frst group on day 3. Conclusions During the frst days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
info:eu-repo/grantAgreement/ISCIII/Miguel Servet 2020/ CP20%00041/ES/; https://hdl.handle.net/10017/62197; AR/0000049017; Critical Care; 28; 91 |
| DOI: |
10.1186/s13054-024-04876-5 |
| Availability: |
https://hdl.handle.net/10017/62197; https://doi.org/10.1186/s13054-024-04876-5 |
| Rights: |
© The Author(s) 2024 ; Attribution 4.0 International (CC BY 4.0) ; http://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.ABCFA6CC |
| Database: |
BASE |